What can the hippocampal representation of environmental geometry tell us about Hebbian learning?

Lever, Colin; Burgess, Neil; Cacucci, Francesca; Hartley, Tom T.; O’Keefe, John

doi:10.1007/s00422-002-0360-z

Cited by 42 publications

(54 citation statements)

References 91 publications

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“…We show a single 30 min episode of unsupervised learning, which is known (24) to cause pronounced and lasting changes to behavior, doubled the number of pTrkB+ synapses above values in control rats. The NMDAR antagonist CPP eliminated these increases without reducing movement within the open field, thereby indicating that extensive amounts of exploratory behavior alone do not measurably increase the phosphorylation of synaptic TrkB.…”

Section: Discussionmentioning

confidence: 65%

Learning induces neurotrophin signaling at hippocampal synapses

Chen

Rex²,

Sanaiha³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Learning-induced trophic activity is thought to be critical for maintaining health of the aging brain. We report here that learning, acting through an unexpected pathway, activates synaptic receptors for one of the brain's primary trophic factors. Unsupervised learning, but not exploratory activity alone, robustly increased the number of postsynaptic densities associated with activated (phosphorylated) forms of BDNF's TrkB receptor in adult rat hippocampus; these increases were blocked by an NMDA receptor antagonist. Similarly, stimulation of hippocampal slices at the learning-related theta frequency increased synaptic TrkB phosphorylation in an NMDA receptor-dependent fashion. Theta burst stimulation, which was more effective in this regard than other stimulation patterns, preferentially engaged NMDA receptors that, in turn, activated Src kinases. Blocking the latter, or scavenging extracellular TrkB ligands, prevented theta-induced TrkB phosphorylation. Thus, synaptic TrkB activation was dependent upon both ligand presentation and postsynaptic signaling cascades. These results show that afferent activity patterns and cellular events involved in memory encoding initiate BDNF signaling through synaptic TrkB, thereby ensuring that learning will trigger neurotrophic support.BDNF | NMDA receptor | Src kinase | theta burst stimulation | TrkB E nvironmental enrichment and learning modify brain anatomy (1) and chemistry (2, 3) in older animals and slow cognitive decline and the onset of dementia in aged humans (4). Although multiple factors likely contribute to these effects, it is generally agreed that trophic factors play a central role. These releasable peptides stimulate growth and help maintain neuronal viability (5, 6) and are logical candidates for the agency whereby experience slows age-related deterioration of brain networks (7). However, there is little evidence that learning or learning-related patterns of electrical activity actually stimulate neurotrophic signaling at brain synapses. The absence of tests of the learning/ trophic signaling hypothesis is likely due to difficulties in sampling the activation state of proteins in the very small percentage of synapses affected by physiologically relevant patterns of activity or by learning itself (8, 9). Recent advances in restorative deconvolution microscopy have partly obviated these problems (8, 10). We used these techniques in the present studies to test the hypothesis that learning and learning-related brain rhythms cause the rapid activation of BDNF's TrkB receptor within synapses of the adult hippocampus.BDNF is of particular interest with regard to experience and aging because its expression by cortical neurons is both regulated by activity (11) and related to age-associated changes in brain anatomy and functioning (12). Accordingly, we focused on BDNF and asked if a 30 min period of a ubiquitous form of mammalian learning (i.e., unsupervised learning of a novel environment) increases the number of synapses containing the activated form of TrkB (13). W...

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Section: Discussionmentioning

confidence: 65%

Learning induces neurotrophin signaling at hippocampal synapses

Chen

Rex²,

Sanaiha³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Barry et al (2006), see also and Lever et al (2002a), described how placement of a barrier into a square environment initially caused some place fields to be duplicated, and subsequently how most duplicates were lost (Fig. 4A).…”

Section: Simulation: Experience-dependent Changes In Pc Firing In a Smentioning

confidence: 94%

“…The thresholded sum of a population of these cells provides a good analogue of PC firing and makes accurate predications as to how place fields respond to geometric manipulation of an environment. Predictions include: the duplication of place fields by addition of a barrier (Lever et al, 2002a;Barry et al, 2006); that some fields will follow the shape of existing barriers (e.g. crescent fields in a circular enclosure) ; and specific predictions as to how individual fields would change when the shape of an enclosure was changed (Hartley et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Learning in a geometric model of place cell firing

Barry

Burgess

2007

Hippocampus

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Following Hartley et al. (Hartley et al. (2000) Hippocampus 10:369-379), we present a simple feed-forward model of place cell (PC) firing predicated on neocortical information regarding the environmental geometry surrounding the animal. Incorporating the idea of boundaries with distinct sensory qualities, we show that synaptic plasticity mediated by a BCM-like rule (Bienenstock et al. (1982) J Neurosci 2:32-48) produces PCs that encode position relative to specific extended landmarks. In an unchanging environment the model is shown to undergo an initial phase of learning, resulting in the formation of stable place fields. In familiar environments, perturbation of environmental cues produces graded changes in the firing rate and position of place fields. Model simulations are compared favorably with three sets of experimental data: (1) Results published by Barry et al. (Barry et al. (2006) Rev Neurosci 17:71-97) showing the slow disappearance of duplicate place fields produced when a barrier is placed into a familiar environment. (2) Rivard et al.'s (Rivard et al. (2004) J Gen Physiol 124:9-25) study showing a graded response in PC firing such that fields near to a centrally placed object encode space relative to the object, whereas more distant fields respond to the surrounding environment. (3) Fenton et al.'s (Fenton et al. (2000a) J Gen Physiol 116:191-209) observation that inconsistent rotation of cue cards produces parametric changes in place field positions. The merits of the model are discussed in terms of its extensibility and biological plausibility.

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“…In humans, the hippocampus has been specifically implicated in accurate spatial navigation, both in terms of the effects of lesions and metabolic activity during virtual navigation, although showing a greater (right) lateralization of function than in rodents (Burgess et al, 2002). Indeed, virtual reality analogs of behavioral tests in rodents show similar associations of hippocampal and striatal activity with place and response learning, respectively.…”

Section: Brain Regions Associated With Spatial Navigationmentioning

confidence: 96%

The Cognitive Architecture of Spatial Navigation: Hippocampal and Striatal Contributions

Chersi

Burgess

2015

Neuron

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193

176

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Spatial navigation can serve as a model system in cognitive neuroscience, in which specific neural representations, learning rules, and control strategies can be inferred from the vast experimental literature that exists across many species, including humans. Here, we review this literature, focusing on the contributions of hippocampal and striatal systems, and attempt to outline a minimal cognitive architecture that is consistent with the experimental literature and that synthesizes previous related computational modeling. The resulting architecture includes striatal reinforcement learning based on egocentric representations of sensory states and actions, incidental Hebbian association of sensory information with allocentric state representations in the hippocampus, and arbitration of the outputs of both systems based on confidence/uncertainty in medial prefrontal cortex. We discuss the relationship between this architecture and learning in model-free and model-based systems, episodic memory, imagery, and planning, including some open questions and directions for further experiments.

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What can the hippocampal representation of environmental geometry tell us about Hebbian learning?

Cited by 42 publications

References 91 publications

Learning induces neurotrophin signaling at hippocampal synapses

Learning induces neurotrophin signaling at hippocampal synapses

Learning in a geometric model of place cell firing

The Cognitive Architecture of Spatial Navigation: Hippocampal and Striatal Contributions

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